Kaplan-Meier Plot
Chart overview
The Kaplan-Meier estimator is the cornerstone of survival analysis in clinical research, epidemiology, and preclinical oncology studies.
Key points
- Each step-down in the curve represents one or more events (death, relapse, failure) occurring at that time point.
- Tick marks on the curve indicate censored observations - patients who left the study or had not yet experienced the event at last follow-up.
- Multiple groups can be compared on the same plot, and a log-rank test p-value is typically reported to assess whether survival distributions differ significantly.
Python Tutorial
How to create a kaplan-meier plot in Python
Use the full tutorial for implementation details, troubleshooting, and chart variations in matplotlib, seaborn, and plotly.
Python Scatter Plot TutorialExample Visualization
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"Create a Kaplan-Meier survival plot comparing treatment vs. control groups from my clinical data. Show step-function curves with 95% confidence interval shading. Add tick marks for censored observations. Compute and display the log-rank test p-value. Mark median survival times with dashed lines. Label axes as 'Time (months)' and 'Survival Probability'. Format for publication at 300 DPI."
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Python Code Example
Console Output
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Common Use Cases
- 1Overall survival and progression-free survival in oncology clinical trials
- 2Device or implant failure analysis in biomedical engineering studies
- 3Time-to-relapse curves in psychiatric or infectious disease cohort studies
- 4Preclinical tumor growth delay studies comparing treatment arms in mice
Pro Tips
Always display the number at risk table below the x-axis for clinical transparency
Use the log-rank test for group comparisons but report hazard ratios from Cox regression for effect size
Separate curves that cross each other are better analyzed with restricted mean survival time
Confidence intervals widen toward the tail when few patients remain - truncate at a meaningful follow-up horizon
Long-tail keyword opportunities
High-intent chart variations
Library comparison for this chart
matplotlib
Best when you need full control over axis formatting, annotation placement, and journal-specific styling for kaplan-meier-plot.
numpy
Useful in specialized workflows that complement core Python plotting libraries for kaplan-meier-plot analysis tasks.
pandas
Good for quick exploratory drafts directly from DataFrame operations before polishing in matplotlib or plotly.
lifelines
Useful in specialized workflows that complement core Python plotting libraries for kaplan-meier-plot analysis tasks.
Scientific Chart Selection Cheat Sheet
Not sure whether to use a Violin Plot, Box Plot, or Ridge Plot? Download our single-page reference mapping the most-used scientific chart types, exactly when to use them, and the core Matplotlib/Seaborn functions.